Getting Started With Seaborn: Install, Import, And Usage

Seaborn Library in Python

Seaborn is a visualization library for plotting good-looking and stylish graphs in Python. It provides different types of styles and color themes to make good-looking graphs. The latest version of the seaborn library is 0.11.2.

Installation

pip install seaborn

Mandatory dependencies

numpy (>= 1.9.3)
scipy (>= 0.14.0)
matplotlib (>= 1.4.3)
pandas (>= 0.15.2)

Importing Seaborn Library

import seaborn as sb

Using Seaborn Library for Creating Graphs

Creating Line Graph

# Importing Seaborn and Matplotlib Library For Graph Plotting
import seaborn as sb
import matplotlib.pyplot as plt

# Setting theme of our Graph using set_theme() function
sb.set_theme(style="darkgrid")

# Creating a List of Data for Graph Plotting
graph_data = [1, 750, 250, 2400, 2000]

# Plotting a Line Graph Using Seaborn Library
sb.lineplot(data = graph_data)

# Showing the plotted graph using show() function of matplolib library
plt.show()

Output:

Creating Scattered Line Graph

# Importing Seaborn Matplotlib and Numpy Library For Graph Plotting
import seaborn as sb
import matplotlib.pyplot as plt
import numpy as np

# Randomising the data to get random x and y co-ordinates for plotting
rs = np.random.RandomState(7)
x = rs.normal(2, 1, 75)
y = 2 + 1.5 * x + rs.normal(0, 2, 75)

# Plotting x and y co-ordinates using residpolt() function
sb.residplot(x=x, y=y)

# Showing the plotted graph using show() function of matplolib library
plt.show()

Output:

Creating BoxPlot Graphs

# Importing Seaborn and Matplotlib Library For Graph Plotting
import seaborn as sb
import matplotlib.pyplot as plt

# Setting the style of our Graph using set() function
sb.set_theme(style="whitegrid")

# Using load_datatset() function to get data for graph plotting
example = sb.load_dataset("tips")

# Plotting a Line Graph Using Seaborn Library
sb.boxplot(x="day", y="total_bill",
            hue="smoker", palette=["m", "g"],
            data=example)
sb.despine(offset=10, trim=True)

# Showing the plotted graph using show() function of matplolib library
plt.show()

Output: